Certain embodiments of the present technology relate to generation of teaching files from healthcare information systems. More particularly, certain embodiments relate to methods and systems for automatically selecting potential teaching file images and using a search engine to produce supporting data.
In medical environments, teaching is often linked with diagnosis and treatment. For example, a medical practitioner will often generate information to be included in a medical teaching file while reviewing images, such as radiology scans, where the practitioner believes the images can be useful in teaching a particular medical subject matter. The generation of these teaching files can be important building blocks for clinical support, teaching and research.
The process for generation of a teaching file is often initiated by a medical practitioner. The practitioner will assemble a package of images and data, also known as a teaching file message, and providing the message to a user that will create a teaching file using the information in the teaching file message. The teaching file message is generally comprised of medical images, and supporting data such as reports, graphics and other information helping to portray and explain the subject matter at hand. The duties of a medical practitioner in the generation of a teaching file therefore generally involve selecting medical images, and searching for, or generating the supporting data.
In prior methods of non-electronic teaching file generation, a practitioner reviewing film would take certain images that the practitioner deemed appropriate and copy the images into a paper folder with accompanying notes. However, as medical imaging technology moved into a digital environment, the teaching file creation process began to adapt. In digital environments such as a Picture Archiving and Communication Systems (PACS), images may be manipulated and cross-referenced in a powerful and interactive fashion. A PACS is a clinical image archive system and may comprise a series of computers or networks dedicated to the storage, retrieval, distribution and presentation of images. The medical images are stored in an independent format. As more hospitals adopt PACS, a scalable repository of case-based files will become a valuable tool for on-demand learning and exchange of data. Teaching files may be created by compiling a series of medical images, such as radiological scans, and adding data, text, graphics, charts or other information to serve as a reference guide for future users.
To assist in adapting to the newer technological environments, the Integrating the Healthcare Enterprise (IHE) initiative was established. The IHE is an initiative by healthcare organizations and vendors, supported by the Radiological Society of North America (RSNA) to improve the way computer systems in healthcare share information. One of the achievements of the IHE pertaining to the creation of teaching files is the establishment of a Teaching File and Clinical Trial Export (TCE) profile. IHE-TCE defines standards for gathering together teaching file images and related information for automatic routing to teaching file servers. For example, the process of generating a teaching file from a teaching file message is an artifact of the IHE-TCE profile. However, the IHE-TCE profile leaves the tasks of initial image selection and data entry as a manual processes to be performed by the radiologist. For example, a radiologist is to review each image, flagging images the radiologist deems appropriate for the teaching file, and further entering data or other information.
Another shortcoming of present teaching file generation methods is that practitioners do not typically know the final outcome, diagnosis or pathology for a particular case at the time of interpretation. Therefore, it may take several days to complete a teaching file message under this method. Furthermore, practitioners do not always realize that a particular case is worth documenting as a teaching file during the interpretation process. When the final diagnosis or interesting information does become available, the practitioners may have to manually research back to find the case to generate a teaching file, thereby disrupting workflow.
Presently, to generate images for the teaching file message, the medical practitioner must either select images and data that are to be included in the file message either during the interpretation process (a process of interpreting or reading medical or radiological images) or a medical study or retrieve the images and data after the interpretation process is completed. Each medical study, such as a computed tomography (CT) study or a Magnetic Resonance Imaging (MRI) study, may contain hundreds or thousands of individual images, only a few of which may be valuable for use in a teaching file. Thus, in order to create a teaching file of clinical images the radiologist will have to document or remember each image deemed appropriate for teaching as it is recorded or reviewed. Such a process is burdensome and can detract from the radiologist's ultimate objectives.
Alternatively, the practitioner may spend additional time reviewing images for teaching file generation after they have already been viewed once. In either situation, the selection of images for the teaching file generation process is time consuming and burdensome on the practitioner, and leaves high room for error in both the interpretation process and in the generation of a teaching file. Thus, there exists a need to provide a system that will automatically flag and identify images in a PACS appropriate for a teaching file based on properties associated with the images.
As mentioned, medical practitioners must also generate supporting data to accompany the medical images in the teaching file messages. Supporting data may include already existing radiology report, teaching files, medical reports or other clinical information related to the subject matter of the teaching file. However, supporting data may be stored in multiple systems that are outside of the PACS, and thus be very difficult for a practitioner to efficiently retrieve. To gather this data, the practitioner must browse through each system containing the information until the desired data to accompany the images is found.
Thus, gathering data on clinical systems can be a difficult task. Current clinical or image-related systems often organize data in a format determined by developers that is unusable by one or more medical practitioners in the field. Additionally, information may be stored in a format that does not lend itself to data retrieval and usage in other contexts. These problems make the teaching file creation process difficult and time consuming, and can be disruptive the interpretation process.
An increasing number of medical information systems require free text search capability for searching finding information about a specific medical diagnosis, patient demographics, decease statistics, etc. However, these search engines are not customized for searching electronic medical records. Therefore, there is a need for systems and methods for free text searching capability with electronic medical records to obtain supporting data accompanying images in a teaching file message.